Abstract
There is an increasing demand for Internet of Things (IoT) networks consisting of resource-constrained devices executing increasingly complex applications. Due to these resource constraints, IoT devices will not be able to execute expensive tasks. One solution is to offload expensive tasks to resource-rich edge nodes, which requires a framework that facilitates the selection of suitable edge nodes to perform task offloading. Therefore, in this article, we present a novel trust-model-driven system architecture , based on behavioral evidence , that is suitable for resource-constrained IoT devices and supports computation offloading. We demonstrate the viability of the proposed architecture with an example deployment of the Beta Reputation System trust model on real hardware to capture node behaviors. The open environment of edge-based IoT networks means that threats against edge nodes can lead to deviation from expected behavior. Hence, we perform a threat modeling to identify such threats. The proposed system architecture includes threat handling mechanisms that provide security properties such as confidentiality, authentication, and non-repudiation of messages in required scenarios and operate within the resource constraints. We evaluate the efficacy of the threat handling mechanisms and identify future work for the standards used.
Highlights
Internet of Things (IoT) networks—comprising a large number of IoT devices—are being deployed in a variety of contexts including smart farming [29], healthcare [28], and smart cities [43]
The implementation is limited by the RAM of the IoT hardware because dynamic memory allocation is typically avoided with embedded systems, as long-term use can lead to memory fragmentation, which prevents future allocation requests from succeeding
Results for AES-CCM encryption and decryption were gathered by generating a random plaintext with a random length from 1 to 1 024 B, a random 35 B of additional authenticated data, a random 16 B key, and a random 13 B nonce
Summary
Internet of Things (IoT) networks—comprising a large number of IoT devices—are being deployed in a variety of contexts including smart farming [29], healthcare [28], and smart cities [43]. An issue is that many of these devices are resource constrained, with limited processing power, data storage, energy storage, and other constraints Due to these resource constraints, it is infeasible for IoT devices to perform computationally expensive tasks such as machine learning. To enable the IoT devices to execute such computeintensive applications, a support network, called an edge network, is used that consists of resourcerich (powerful) nodes called edge nodes.2 These tasks will need to be sent from IoT devices to the edge network in order to be executed, in a process called computation offloading. For a large class of applications, offloading to edge nodes is preferred, such as when the network has no access to Cloud services or when latency is important
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
